Aspect-Oriented Incremental Customization of Middleware Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As distributed applications evolve, incremental customization of middleware services is often required; these customizations should be unpluggable, modular, and efficient. This is difficult to achieve because the customizations depend on both application-specific needs and the services provided. Although middleware allows programmers to separate application-specific functionality from lower-level details, traditional methods of customization do not allow efficient modularization. Currently, making even minor changes to customize middleware is complicated by the lack of locality. Programmers may have to compromise between the two extremes: to interpose a simple, well-localized layer of functionality between the application and middleware, or to make a large number of small, poorly localized, invasive changes to all execution points which interact with middleware services. Although the invasive approach allows a more efficient customization, it is harder to ensure consistency, more tedious to implement, and exceedingly difficult to unplug. Thus, a common approach is to add an extra layer for systemic concerns such as robustness, caching, filtering, and security. Aspect-oriented programming (AOP) offers a potential alternative between the interposition and invasive approaches by providing modular support for the implementation of crosscutting concerns. AOP enables the implementation of efficient customizations in a structured and unpluggable manner. We demonstrate this approach by comparing traditional and AOP customizations of fault tolerance in a distributed file system model, JNFS. Our results show that using AOP can reduce the amount of invasive code to almost zero, improve efficiency by leveraging the existing application behaviour, and facilitate incremental customization and extension of middleware services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it